The knowledge-augmented deep learning paradigm refers to a paradigm in which domain knowledge is identified and integrated into deep models. Conventional methods typically employ task-specific approaches to gather external knowledge from various sources. In contrast, large language models are extensively pre-trained and can serve as a comprehensive source of external knowledge. In this paper, we propose CoT-KA, a Chain-of-Thought-based method that augments knowledge for deep learning. CoT-KA avoids the need for additional knowledge retrieval or knowledge reasoning models, as required in conventional augmentation methods. Our results demonstrate that CoT-KA outperforms both pure CoT-based methods and the non-augmented method across the majority of eleven publicly available benchmarks for various reasoning tasks.

翻译：知识增强深度学习范式是指将领域知识识别并整合到深度模型中的范式。传统方法通常采用特定任务的方法从各类来源收集外部知识。相比之下，大规模语言模型经过广泛预训练，可作为全面的外部知识源。本文提出CoT-KA，一种基于思维链的知识增强深度学习方法。与传统增强方法不同，CoT-KA无需额外的知识检索或知识推理模型。实验结果表明，在针对多种推理任务的十一个公开基准测试中，CoT-KA在大多数任务上均优于纯思维链方法和非增强方法。